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wPINNs: Weak Physics informed neural networks for approximating entropy solutions of hyperbolic conservation laws

18 July 2022

Papers citing "wPINNs: Weak Physics informed neural networks for approximating entropy solutions of hyperbolic conservation laws"

21 / 21 papers shown

Title
A Weak Penalty Neural ODE for Learning Chaotic Dynamics from Noisy Time Series Xuyang Li John Harlim Romit Maulik R. Maulik AI4TS 232 0 0 10 Nov 2025
SPIKE: Stable Physics-Informed Kernel Evolution Method for Solving Hyperbolic Conservation Laws Hua Su Lei Zhang Jin Zhao 72 0 0 21 Oct 2025
THINNs: Thermodynamically Informed Neural Networks Javier Castro Benjamin Gess PINN 136 0 0 23 Sep 2025
DeepWKB: Learning WKB Expansions of Invariant Distributions for Stochastic Systems Yao Li Yicheng Liu Shirou Wang 82 0 0 13 Aug 2025
Non-Asymptotic Stability and Consistency Guarantees for Physics-Informed Neural Networks via Coercive Operator Analysis Ronald Katende 76 0 0 16 Jun 2025
(U)NFV: Supervised and Unsupervised Neural Finite Volume Methods for Solving Hyperbolic PDEs Nathan Lichtlé Alexi Canesse Zhe Fu Hossein Nick Zinat Matin M. D. Monache Alexandre M. Bayen 120 0 0 29 May 2025
Challenges and Advancements in Modeling Shock Fronts with Physics-Informed Neural Networks: A Review and Benchmarking Study J. Abbasi Ameya D. Jagtap Ben Moseley Aksel Hiorth P. Andersen PINN AI4CE 211 11 0 14 Mar 2025
Coupled Integral PINN for conservation law Yeping Wang Shihao Yang PINN 181 1 0 18 Nov 2024
GoRINNs: Godunov-Riemann Informed Neural Networks for Learning Hyperbolic Conservation LawsJournal of Computational Physics (JCP), 2024 Dimitrios G. Patsatzis Mario di Bernardo L. Russo Constantinos Siettos AI4CE 274 5 0 29 Oct 2024
Physics-Informed GNN for non-linear constrained optimization: PINCO a solver for the AC-optimal power flow Anna Varbella Damien Briens B. Gjorgiev Giuseppe Alessio DÍnverno G. Sansavini 123 4 0 07 Oct 2024
Beyond Derivative Pathology of PINNs: Variable Splitting Strategy with Convergence Analysis Yesom Park Changhoon Song Myungjoo Kang 193 3 0 30 Sep 2024
Error analysis for finite element operator learning methods for solving parametric second-order elliptic PDEs Youngjoon Hong Seungchan Ko Jae Yong Lee 200 3 0 27 Apr 2024
An operator preconditioning perspective on training in physics-informed machine learningInternational Conference on Learning Representations (ICLR), 2023 Tim De Ryck Florent Bonnet Siddhartha Mishra Emmanuel de Bezenac AI4CE 307 21 0 09 Oct 2023
Spectral-Bias and Kernel-Task Alignment in Physically Informed Neural Networks Inbar Seroussi Asaf Miron Zohar Ringel PINN 301 2 0 12 Jul 2023
A Deep Learning Framework for Solving Hyperbolic Partial Differential Equations: Part I Rajat Arora PINN AI4CE 142 1 0 09 Jul 2023
Conservative Physics-Informed Neural Networks for Non-Conservative Hyperbolic Conservation Laws Near Critical States Reyna Quita Yu-Shuo Chen Hsin-Yi Lee John M. Hong PINN 123 0 0 22 May 2023
$PINNs error estimates for nonlinear equations in $\mathbb{R}$-smooth Banach spaces$ PINNs error estimates for nonlinear equations in $\mathbb{R}$ -smooth Banach spaces Jiexing Gao Yurii Zakharian 205 2 0 18 May 2023
Nonlinear Reconstruction for Operator Learning of PDEs with DiscontinuitiesInternational Conference on Learning Representations (ICLR), 2022 S. Lanthaler Roberto Molinaro Patrik Hadorn Siddhartha Mishra 312 28 0 03 Oct 2022
Variationally Mimetic Operator NetworksComputer Methods in Applied Mechanics and Engineering (CMAME), 2022 Dhruv V. Patel Deep Ray M. Abdelmalik T. Hughes Assad A. Oberai 311 33 0 26 Sep 2022
Error analysis for deep neural network approximations of parametric hyperbolic conservation laws Tim De Ryck Siddhartha Mishra PINN 162 15 0 15 Jul 2022
Iterative Surrogate Model Optimization (ISMO): An active learning algorithm for PDE constrained optimization with deep neural networksComputer Methods in Applied Mechanics and Engineering (CMAME), 2020 K. Lye Siddhartha Mishra Deep Ray P. Chandrasekhar 229 85 0 13 Aug 2020

wPINNs: Weak Physics informed neural networks for approximating entropy solutions of hyperbolic conservation laws

18 July 2022

Papers citing "wPINNs: Weak Physics informed neural networks for approximating entropy solutions of hyperbolic conservation laws"

21 / 21 papers shown

Title
A Weak Penalty Neural ODE for Learning Chaotic Dynamics from Noisy Time Series Xuyang Li John Harlim Romit Maulik R. Maulik AI4TS 232 0 0 10 Nov 2025
SPIKE: Stable Physics-Informed Kernel Evolution Method for Solving Hyperbolic Conservation Laws Hua Su Lei Zhang Jin Zhao 72 0 0 21 Oct 2025
THINNs: Thermodynamically Informed Neural Networks Javier Castro Benjamin Gess PINN 136 0 0 23 Sep 2025
DeepWKB: Learning WKB Expansions of Invariant Distributions for Stochastic Systems Yao Li Yicheng Liu Shirou Wang 82 0 0 13 Aug 2025
Non-Asymptotic Stability and Consistency Guarantees for Physics-Informed Neural Networks via Coercive Operator Analysis Ronald Katende 76 0 0 16 Jun 2025
(U)NFV: Supervised and Unsupervised Neural Finite Volume Methods for Solving Hyperbolic PDEs Nathan Lichtlé Alexi Canesse Zhe Fu Hossein Nick Zinat Matin M. D. Monache Alexandre M. Bayen 120 0 0 29 May 2025
Challenges and Advancements in Modeling Shock Fronts with Physics-Informed Neural Networks: A Review and Benchmarking Study J. Abbasi Ameya D. Jagtap Ben Moseley Aksel Hiorth P. Andersen PINN AI4CE 211 11 0 14 Mar 2025
Coupled Integral PINN for conservation law Yeping Wang Shihao Yang PINN 181 1 0 18 Nov 2024
GoRINNs: Godunov-Riemann Informed Neural Networks for Learning Hyperbolic Conservation LawsJournal of Computational Physics (JCP), 2024 Dimitrios G. Patsatzis Mario di Bernardo L. Russo Constantinos Siettos AI4CE 274 5 0 29 Oct 2024
Physics-Informed GNN for non-linear constrained optimization: PINCO a solver for the AC-optimal power flow Anna Varbella Damien Briens B. Gjorgiev Giuseppe Alessio DÍnverno G. Sansavini 123 4 0 07 Oct 2024
Beyond Derivative Pathology of PINNs: Variable Splitting Strategy with Convergence Analysis Yesom Park Changhoon Song Myungjoo Kang 193 3 0 30 Sep 2024
Error analysis for finite element operator learning methods for solving parametric second-order elliptic PDEs Youngjoon Hong Seungchan Ko Jae Yong Lee 200 3 0 27 Apr 2024
An operator preconditioning perspective on training in physics-informed machine learningInternational Conference on Learning Representations (ICLR), 2023 Tim De Ryck Florent Bonnet Siddhartha Mishra Emmanuel de Bezenac AI4CE 307 21 0 09 Oct 2023
Spectral-Bias and Kernel-Task Alignment in Physically Informed Neural Networks Inbar Seroussi Asaf Miron Zohar Ringel PINN 301 2 0 12 Jul 2023
A Deep Learning Framework for Solving Hyperbolic Partial Differential Equations: Part I Rajat Arora PINN AI4CE 142 1 0 09 Jul 2023
Conservative Physics-Informed Neural Networks for Non-Conservative Hyperbolic Conservation Laws Near Critical States Reyna Quita Yu-Shuo Chen Hsin-Yi Lee John M. Hong PINN 123 0 0 22 May 2023
$PINNs error estimates for nonlinear equations in $\mathbb{R}$-smooth Banach spaces$ PINNs error estimates for nonlinear equations in $\mathbb{R}$ -smooth Banach spaces Jiexing Gao Yurii Zakharian 205 2 0 18 May 2023
Nonlinear Reconstruction for Operator Learning of PDEs with DiscontinuitiesInternational Conference on Learning Representations (ICLR), 2022 S. Lanthaler Roberto Molinaro Patrik Hadorn Siddhartha Mishra 312 28 0 03 Oct 2022
Variationally Mimetic Operator NetworksComputer Methods in Applied Mechanics and Engineering (CMAME), 2022 Dhruv V. Patel Deep Ray M. Abdelmalik T. Hughes Assad A. Oberai 311 33 0 26 Sep 2022
Error analysis for deep neural network approximations of parametric hyperbolic conservation laws Tim De Ryck Siddhartha Mishra PINN 162 15 0 15 Jul 2022
Iterative Surrogate Model Optimization (ISMO): An active learning algorithm for PDE constrained optimization with deep neural networksComputer Methods in Applied Mechanics and Engineering (CMAME), 2020 K. Lye Siddhartha Mishra Deep Ray P. Chandrasekhar 229 85 0 13 Aug 2020